Models for Rapid estimates of leaf litter chemistry using reflectance spectroscopy

Measuring the chemical traits of leaf litter is important for understanding plants’ roles in nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression (PLSR) models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation R2 = 0.543-0.941; %RMSE = 7.49-18.5) and ground-leaf litter spectra (validation R2 = 0.491-0.946; %RMSE = 7.00-19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results ..., This repository contains coefficients for partial least-squares regression models trained to predict leaf litter traits from spectra of intact or ground leaves. Models for intact leaves were trained either on the full spectrum (400-2400 nm) or subsets (visible and near-infrared, 400-1000 nm; short-wave infrared, 1300-2400 nm)., , # Models for rapid estimates of leaf litter chemistry using reflectance spectroscopy

This folder contains the partial least-squares regression (PLSR) model coefficients to accompany the paper \"Rapid estimates of leaf litter chemistry using reflectance spectroscopy\" by Kothari et al. (2024) *Canadian Journal of Forest Research*. An open version is on *bioRxiv*, DOI: 10.1101/2023.11.27.568939.

Each set of models for a given trait comprises a .csv file containing 200 models (rows) **×** coefficients (columns). The 200 models are derived from a jackknife analysis described in the paper. To generate trait estimates using a model set, you can apply them to the data (see below) and use the mean or the full distribution of estimates.

## Spectral data preparation

Your spectral data should ideally be processed in the way described by the paper linked above. At a minimum, the data must be resampled to a 1 nm continuously in the 400-2400 nm range, or 400-1000 nm (VNIR) or 1300-2400 nm (SWIR) fo...